langchain-memoryrouter 0.1.0

MemoryRouter integration for LangChain and LangGraph

langchain-memoryrouter

MemoryRouter memory for LangChain and LangGraph.

MemoryRouter gives every user in your AI product a private memory vault. This package lets LangChain developers use that memory layer through tools, graph nodes, or a LangChain BaseStore.

Install

pip install langchain-memoryrouter

Get a Memory Key at memoryrouter.ai. In multi-user products, map each app user to their own Memory Key.

Storage hygiene

Every write path sanitizes messages before ingest. MemoryRouter stores conversation text only:

• Keeps HumanMessage text as user
• Keeps AIMessage text as assistant
• Drops ToolMessage entirely
• Drops AI tool calls and function-call args
• Drops AI messages that are only tool calls with no text

This is enforced in tools, nodes, and store writes. It is not a config option.

Pattern 1: tools

Works with LangChain bind_tools() and LangGraph create_react_agent().

from langchain_memoryrouter import create_memory_tools

tools = create_memory_tools(memory_key="mk_user_123")
model_with_tools = model.bind_tools(list(tools))

The package exposes two primitives only:

• memoryrouter_retain stores sanitized conversation text with /v1/memory/ingest
• memoryrouter_recall searches memory with /v1/memory/search

No reflect tool. MemoryRouter is retain and recall.

Pattern 2: LangGraph nodes

Use nodes when you want memory to run automatically in the graph.

from langchain_memoryrouter import create_recall_node, create_retain_node

recall_node = create_recall_node(memory_key_config_key="memory_key")
retain_node = create_retain_node(memory_key_config_key="memory_key")

result = graph.invoke(
    {"messages": messages},
    config={"configurable": {"memory_key": "mk_user_123", "thread_id": "chat_abc"}},
)

create_recall_node calls /v1/memory/prepare and returns a ready-to-inject memory context block. create_retain_node sanitizes and calls /v1/memory/ingest.

Pattern 3: BaseStore

Use MemoryRouterStore as a semantic long-term store for LangGraph.

from langchain_memoryrouter import MemoryRouterStore

store = MemoryRouterStore(memory_key="mk_user_123")
graph = builder.compile(store=store)

MemoryRouter is semantic memory, not a literal KV database. mset retains text into the user's vault. mget searches by key and returns the best matching memory content.

API targeted

Base URL: https://api.memoryrouter.ai

Auth: Authorization: Bearer mk_xxx

Retain:

POST /v1/memory/ingest
{
  "messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}],
  "session_id": "optional",
  "model": "optional",
  "embeddings": "optional"
}

Recall:

POST /v1/memory/search
{"query": "what does the user prefer", "limit": 10}

Graph recall context:

POST /v1/memory/prepare
{
  "messages": [{"role": "user", "content": "..."}],
  "session_id": "optional",
  "density": "default",
  "context_limit": 10
}